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Towards predictive maintenance of hydrogen pressure vessels based on multi-sensor data fusion and digital twin modeling

  • Hydrogen pressure vessels are among the most essential components for reliable hydrogen technology. Under current regulations, a mostly conservative strategy is employed, restricting the usage time of hydrogen pressure vessels without providing information on the real remaining lifetime. During the service life, pressure vessels are inspected periodically. However, no established method that can provide continuous monitoring or information on the remaining safe service life of the vessel. In this paper, we propose a sensor network for Structural Health Monitoring (SHM) of hydrogen pressure vessels where data from all sensors are collected and centrally evaluated. Specifically, we integrate three different SHM sensing technologies namely Guided Wave ultrasonics (GW), Acoustic Emission testing (AT), and distributed Fiber Optic Sensing (FOS). This integrated approach offers significantly more information and could therefore enable a transition from costly and time-consuming periodicHydrogen pressure vessels are among the most essential components for reliable hydrogen technology. Under current regulations, a mostly conservative strategy is employed, restricting the usage time of hydrogen pressure vessels without providing information on the real remaining lifetime. During the service life, pressure vessels are inspected periodically. However, no established method that can provide continuous monitoring or information on the remaining safe service life of the vessel. In this paper, we propose a sensor network for Structural Health Monitoring (SHM) of hydrogen pressure vessels where data from all sensors are collected and centrally evaluated. Specifically, we integrate three different SHM sensing technologies namely Guided Wave ultrasonics (GW), Acoustic Emission testing (AT), and distributed Fiber Optic Sensing (FOS). This integrated approach offers significantly more information and could therefore enable a transition from costly and time-consuming periodic inspections to more efficient and modern predictive maintenance strategies, including Artificial Intelligence (AI)-based evaluation. This does not only have a positive effect on the operational costs but enhances safety through early identification of critical conditions in the overall system in real-time. We demonstrate an experimental set-up of a lifetime test where a Type IV Composite Overwrapped Pressure Vessel (COPV) is investigated under cyclic loading instrumented with AT, FOS, and GW methods. We acquired data from the sensor network until the pressure vessel failed due to material degradation. The data collected using the three different SHM sensor technologies is planned to be evaluated individually, using data fusion, and AI. In the future, we aim to integrate the measurement setup into a hydrogen refueling station with the data stream implemented into a digital signal processing chain and a digital twin.zeige mehrzeige weniger

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Metadaten
Autor*innen:Christos KarapanagiotisORCiD
Koautor*innen:Jan Heimann, Eric Duffner, Amir Charmi, Marcus Schukar, Seyedreza Hashemi, Jens Prager
Dokumenttyp:Vortrag
Veröffentlichungsform:Präsentation
Sprache:Englisch
Jahr der Erstveröffentlichung:2024
Organisationseinheit der BAM:3 Gefahrgutumschließungen; Energiespeicher
3 Gefahrgutumschließungen; Energiespeicher / 3.5 Sicherheit von Gasspeichern
8 Zerstörungsfreie Prüfung
8 Zerstörungsfreie Prüfung / 8.4 Akustische und elektromagnetische Verfahren
8 Zerstörungsfreie Prüfung / 8.6 Faseroptische Sensorik
DDC-Klassifikation:Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Angewandte Physik
Freie Schlagwörter:Acoustic emission; Fiber optic sensors; Hydrogen; Machine learning; Pressure vessels; Structural health monitoring; Ultrasonic guided waves
Themenfelder/Aktivitätsfelder der BAM:Energie
Energie / Wasserstoff
Veranstaltung:11th European Workshop on Structural Health Monitoring
Veranstaltungsort:Potsdam, Germany
Beginndatum der Veranstaltung:10.06.2024
Enddatum der Veranstaltung:13.06.2024
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:18.06.2024
Referierte Publikation:Nein
Eingeladener Vortrag:Nein
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